A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

You've got two main options:

Currently, it seems as if Fedora doesn't install udev-rules, to get this working, I've had to manually install them from a community project. I'm not very knowledgable in Linux, these are just the steps I had to do to get things working that I'd like to document.

Install android-tools from DNF.

sudo dnf install android-tools

Last updated: July 5, 2026

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See

Léna Piquet (Froyok), [Breakdown] Shadow of the Colossus (PAL, PS2), 2012 https://www.froyok.fr/blog/2012-10-breakdown-shadow-of-the-colossus-pal-ps2/

The Making of Shadow of the Colossus (PDF, hosted with the breakdown above) https://www.froyok.fr/blog/2012-10-breakdown-shadow-of-the-colossus-pal-ps2/resources/making_of_sotc.pdf

Fumito Ueda interview, The Making of the Colossi https://glitterberri.com/extended-developer-interview/making-of-the-colossi/

Executable and Linkable Format (ELF), is the default binary format on Linux-based systems.

ARKit face tracking is shrouded in a ton of myth and mystique.

I reverse engineered the internal face tracking API, and this is how it actually works.

This is based on reverse engineering the API of Apple's face detection framework and testing it with my own code. Some of it is educated guesswork based on names and strings. I did not reverse engineer the details of how ARKit/FaceKit works, but given what I've seen, I'm quite confident in the overall big picture.